{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    " y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.10083842,  0.20064886,  0.2984138 ,  0.39313661,\n",
       "        0.48385164,  0.56963411,  0.64960951,  0.72296256,  0.78894546,\n",
       "        0.84688556,  0.8961922 ,  0.93636273,  0.96698762,  0.98775469,\n",
       "        0.99845223,  0.99897117,  0.98930624,  0.96955595,  0.93992165,\n",
       "        0.90070545,  0.85230712,  0.79522006,  0.73002623,  0.65739025,\n",
       "        0.57805259,  0.49282204,  0.40256749,  0.30820902,  0.21070855,\n",
       "        0.11106004,  0.01027934, -0.09060615, -0.19056796, -0.28858706,\n",
       "       -0.38366419, -0.47483011, -0.56115544, -0.64176014, -0.7158225 ,\n",
       "       -0.7825875 , -0.84137452, -0.89158426, -0.93270486, -0.96431712,\n",
       "       -0.98609877, -0.99782778, -0.99938456, -0.99075324, -0.97202182,\n",
       "       -0.94338126, -0.90512352, -0.85763861, -0.80141062, -0.73701276,\n",
       "       -0.66510151, -0.58640998, -0.50174037, -0.41195583, -0.31797166,\n",
       "       -0.22074597, -0.12126992, -0.0205576 ,  0.0803643 ,  0.18046693,\n",
       "        0.27872982,  0.37415123,  0.46575841,  0.55261747,  0.63384295,\n",
       "        0.7086068 ,  0.77614685,  0.83577457,  0.8868821 ,  0.92894843,\n",
       "        0.96154471,  0.98433866,  0.99709789,  0.99969234,  0.99209556,\n",
       "        0.97438499,  0.94674118,  0.90944594,  0.86287948,  0.8075165 ,\n",
       "        0.74392141,  0.6727425 ,  0.59470541,  0.51060568,  0.42130064,\n",
       "        0.32770071,  0.23076008,  0.13146699,  0.03083368, -0.07011396,\n",
       "       -0.17034683, -0.26884313, -0.36459873, -0.45663749, -0.54402111])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13e5d1a4518>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosy = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "siny = y.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y axis')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fh/Hzj3ZRYq+HXBbbgHiL6GjtKti3T5c9aSLPLUynrKqGl+4c4PUvEOiSDz8d05PPD+SxYOdpr1/PxousXw9/+5uOvPJydCAADge8/bYOC77vvm9KALnJf9cfY8uJs7wwuZ9H1/zqo1VwAH+5YwCZhaX8Yek+r1/PytgGxJuMGgU//jH84x+wbp3bp/l8fy6LdmXx2IhudIltesSVq0y/phMDk9vw24XpFJRU+Oy6Nh7kwgWYPl2vT3gxuONbxMfDv/4Fu3bBP//p9mlOF5by95WHGNk7jkmD6ix35xXSOrXjoWGdeW/jSduVdRlsA+JtXngB/u//9BfYDUrKq3hq3h66x0Xw3eu7eljc5QlwCH+a0p+i0kqeX5Tu02vbeIjnntNRUf/9L0RG+vbakyfD2LHwzDNQWOjWKX63KJ0apXh2fB8Pi2uYn47uSeeYcH7+0S7KKqt9fn0rYBsQbxMWpkdibpY4eWnFQT2Vvq0fwYG+/3f1TIjkezd05ePtmXxxyB6JWQqldDTg9OkwYoTvry8Cr7wCH32kE2wbyeoDuSzdk80PbuxuSnJfq+AAXpjcl4yzpcz84pjPr28FbAPiKw4ehDvvhLN1l5muiyN553lzw3HuHpJCWqd2XhR3eb5/YzeS27XihSX7qLHj462DiI6G+s9/zNPQtSuMHKkfV1a6fFhZZTXPLthLl9hwHrmus5fENcw1XWMYlRrPv1cfIa+43DQd/optQHxFaakeiT37rMuH/GXZAUIDHfxkdA8vCmuYkMAAfjq6J/uyipi3I9NULTYusmOHvoEOrTWbv/5V50hVu+YKemvDcU7kX+B3E/v6JGjkcjw5rhdlldW8tPKgqTr8EduA+IoBA3SZ7H/9S9fNaoDtJ8+ydE82jw7vQkxEiA8EXp7x/dvTr0MUf11+0PYH+zs1NboB2oQJjRr1e5XkZJ0TNXt2g7sWlVXy79VHGNEzlmHdYnwg7vJ0iY3gvqEdmb35JAeyi82W41fYBsSX/Pa3unrvb3972d2UUvzp0/1EhwfzyHVdfCTu8jgcwpPjepFZWMq7X54wW47N5Zg1C776SgdwBPlJn4vbb9eDqGefbdCo/WfdMc6VVvKT0T19JK5hfnRTdyJCAnl+sR1M4oxtQHxJTAz88Icwdy7s2VPvbmsO5rHxaAE/ND60/sI13WK4oWcsr3x+mHMX/GRka3MxVVV6gDJwoE5i9RccDt3J8MgRnR9VD/nny5m57ii39Eukbwc3yst7ibbhwfzgxu6sO3SGTUfzzZbjN9gGxNf85Cd6FJZUdymSmhrFi58eIKVdGHcP8W1zKlf45bheFJVV8sY6u9iiX1LbevaZZ/SPtj9x6616HeQPf6h3LeTVNUcorazm8VHmrvvVxX1DOxITEcw/PvNdzx9/x88+YS2Adu20AaknrHHV/lzSs4r48cjupoTtNkSvhNaM65vA218ep6jMnoX4HWfPwrBhMHGi2Uq+jQi89hosW1bnwn72uTLe/vIEtw1O8kiLAk/TKjiAR6/rwheHz7DtpOvRlM0ZszsSjhWRAyJyWER+WcfrL4nIDuN2UEQKnV6rdnptgW+Ve4AlS+DJJy/apJTiX6sPk9S2FRMGtDdJWMN874ZuFJdV8d5Gey3E73jsMV31wN9mH7UMGADdu9f50mtrj1BTo/jRTXW/7g/cN7QjbcOC+MeqQ2ZL8QtM+5SJSADwT2AckArcLSIXVQlUSj2ulBqolBoI/AP42Onl0trXlFITfCbcU2zerBv6ODXf2Xi0gO0nC/nO8C4EBvjpDwDQt0MU1/eIZea6Y5RW2BFZfkFNje7HoZTny7R7muJimDTpovyUsyUVzN58iokDO/h1R8DwkEAevrYznx/IY3fGObPlmI6Zv1JDgMNKqaNKqQpgNnC5effdwCyfKPMFP/4xRETAX/7y9aZ/rzlCTEQwd6QlmyjMNb4/ohv5JRV88NVJs6XYAHzyia69tmiR2UoaJiICMjLgz3/Whg94b+MJSiurmTHcP6IOL8cD13SidWgg/+8zexZipgHpAJxyep5hbPsWItIR6Ax85rQ5VES2iMhGEZlU30VEZIax35a8PD9qkNSmDTz8sI6Lz8hgT+Y51h7M46FrOxMa5AeJXw0wpHM7ruzUltfXHqWiqsZsOS0bpeBPf9KuoZtvNltNw4jobogHD8KCBZRVVvPWhuOM6BlLzwQf1+tyg9ahQUwf1pkV6TkczGnZeSFmGpC65tn11cmYCnyolHL2l6QYTd7vAf4uInVWGlRKva6USlNKpcXGxjZNsaf50Y/0COwf/+Dfq48QGRLIfUPdK7poBt8b0Y3T58rs7HSz+fJLnffx+OP+kXXuClOmQOfO8OKLfLg1g/ySCr7j42KhTWHa1R0JCXTw5vrjZksxFTMNSAbg7KtJAuprPDGVS9xXSqnTxv1RYDUwyPMSvUznzvDEE+R37MaSPVncO7QjrUP9JPHLBW7oEUuvhEjeXH/cbjplJn//u57RPvCA2UpcJzAQnngCvvySL99dwIDkNlzV2bx6b40lOiKEyYM68PG2DM624FYHZhqQr4DuItJZRILRRuJb0VQi0hNoC3zptK2tiIQYj2OAYYA1U0T//Gf+nTKMABEeHNbJbDWNQkSYfk0n9mUVsflYgdlyWialpTogY8YMCPd+syWP8uCDHHjs52wIiOa7w7sg/r74fwkPDutMeVUNs1rwOqBpBkQpVQU8BiwD9gFzlFJ7ReQ5EXGOqrobmK0uHuL2BraIyE7gc+CPSilLGpALFVUs3HCI35zZRHy4dWYftUwc2IGoVkG8teG42VJaJq1a6cTBp54yW0njCQ/n6b6TiEhKYHSfBLPVNJqeCZEM6xbNOxtOUFndMtcBTY0VVUotUUr1UEp1VUq9YGx7Rim1wGmf3yilfnnJcRuUUv2UUgOM+5m+1u4p5m0/zcD9m7nvjedg3jyz5TSaVsEBTB2SzPL0HDILm9773aYRlJdDRYWud+XrZlEeYH+2nrk+Xb6fgJkmlpxvAg8N60x2URlL92SbLcUU/DfZoAWglOLtDcfJvG4UKiUF/v1vsyW5xf1DO6KUshMLfc0bb+h1tJwcs5W4xXsbTxAS6OCGrSvhl7/U7jiLMaJnHJ2iw/hvC204ZRsQE9l0rIADOcXcf20XZMYMWLVKhzZajKS2YYxKjWfW5pN2qXdfoZTuNZ6UpPuPW4zisko+2ZbJ+AHtCf7hY7pz4ty5ZstqNA6H8OCwzuw4VciOU+617bUytgExkbc3HKdNWBATB3aAhx7SkSmvv262LLeYfk1nCi9UsmBHfYF0Nh7liy9g/37dY8aCfLI9k5KKau4f2lG32+3ZU/fKsSC3De5Aq6AAZm1qeYvptgExiaxzpSxPz+GutGSdOJiYqMs77N9vtjS3GNqlHT3iI/jfJtuN5RNeew2iouCuu8xW0miUUrz75QkGJEUxILmNTiz83vdg0ybYutVseY0mMjSICQPas2DnaYpbWIFR24CYxNwtGVTXKO69yilx8N13rVGKog5EhKlXprAz4xzpp4vMltO8KSiADz+E++6DMP+tG1UfG48WcCj3/MVJs9OmweDB+m+zIHdflUJpZTXzW9gM3DYgJlBTo5iz5RTDukWTEu30AxAaqu/PWbNI2+RBHQgOcDBny6mGd7ZxnzZtYOFCXU/Ngry/+SRRrYIY71xxOipKzz5GjTJPWBMYkBRF78TWvL/pZItKqrUNiAlsOJJPxtlS7qyraOLSpXpRdPdu3wtrIm3DgxnbN4GPt2XYi+nexOHQP7TdupmtpNEUXqhg2d5sJg/qUHfNtwsXdF6LxRAR7hmSTHpWEbszrTkAdAfbgJjAB1tOEdUqiDF1JU8NGaLv33jDt6I8xNQrkykqq+LTFhoX73U2btRdLS3q6lmw8zQVVTV1D54Axozxr1a8jWDioA6EBjmYtbnlLKbbBsTHnC2pYNmey4zAoqP1Yvr77+skMYsxtEs0HaPDmN2Cyzt4lX/+E2bO/MbdaTHmbDlF3w6tSW3fuu4dpkzRhSEtOANvHRrE+P7tmb/jNOfLq8yW4xNsA+Jj5u3IpKK6hruuvEzPj+nTIT/fkgvqDodwZ1oyG48WcOxMidlymhdFRfDRR3DPPZZcPN97+hx7Movqn32Ann0EBcGbb/pOmAeZOiSFCxXVLN7VMhbTbQPiQ5RSfPDVqa8X3Opl1Cho3x7eestn2jzJHVckEeAQexbiaebO1dna06aZrcQt5m7JIDjQcfl2zbGxMGGCjki04Ax8cEobusSE89G2ltHiwDYgPmR35jn2Zxdz5+VmH6B7OsycqTu2WZC41qGM6BnLvO2ZVNe0nIgUr/PWWzrhrnadzEKUV1Uzb0cmY/ok0CYs+PI7P/QQnDkDn312+f38EBFhyhVJbD5WwKmCC2bL8Tq2AfEhH2/LJDjQwa39LzMCq2XsWP1jYVEmD0oip6icL4/kmy2leVBVBV276sxzi5U9B1iRnkPhhUruTEtqeOfRo2HLFr2gbkEmDeqACHy0LcNsKV7HNiA+orK6hoU7TzOqdzxRrVws275hg+4yZ8G48pt6xxEZGsjHLeBL5BMCA/UMxKK5Hx9uzaB9VCjXdI1peOfAQLjiCksaSoAObVpxdZdoPt6W2exzQmwD4iPWHswjv6SCyYPqbPteN7t3625zFizvEBoUwK39E/l0bzYlLSQixWvU1FgyKqmWM+fLWXfoDJMGdSDA4aJRqKjQriyL1oabMjiJkwUX2HLirNlSvIptQHzEx9szaRcezPU9G9GX/a67ICQE3n7be8K8yORBSVyoqGbZXjsnpEl88QX072/JfjEAi3aeprpGMakxg6fgYNi1y7IGZGzfBMKCA/hoa/OegZtqQERkrIgcEJHDIvLLOl6fLiJ5IrLDuD3i9No0ETlk3Pw6LKWorJIV6TmM759IUEAj3vI2bWD8ePjgA+0DtxhpHduS1LYVn2xvGREpXuOddyAiwrJlPj7ZcZrUxNb0iG9k06t779WzbwsWGA0PCWRc30QW78pq1lUZTDMgIhIA/BMYB6QCd4tIah27fqCUGmjc/mMc2w54FrgKGAI8KyJtfSS90SzdnUVFVQ2TB7uwgHgp994LeXmwcqXnhXkZh0O4bVAH1h8+Q/a5MrPlWJOyMh2+O2WK9XqeA8fOlLDzVGHjXLe1TJ2qy7b873+eF+YDpgzuQHF5FSvSrdnwyxXMnIEMAQ4rpY4qpSqA2cBEF48dA6xQShUopc4CK4CxXtLZZD7elkmXmHAGJEU1/uBx42DgQCgu9rwwHzB5cBI1CubvsGchbrFkiU4gtGh5j3nbMxHh4sKJrpKYCDfdpKsyWHAx+qou0cS3DmHBzuabVGimAekAOJdtzTC2XcoUEdklIh+KSG0ChavHIiIzRGSLiGzJy8vzhO5GkXH2ApuOFRihfW5ElYSEwPbtcMcdnhfnAzrHhDMwuY3txnKXOXMgLk43XbIYSinm7cjkmq7RJES5WXrlu9+FW2+1ZLvbAIcwvn97Vh/I5dyF5tknxEwDUtev6aXDjIVAJ6VUf2AlULua7MqxeqNSryul0pRSabGxjVjA9hALd2YBMGmgG1N4Z6qqLNv7euLA9uzPLuZwrjVnUabyn//A4sU6tNVi7DhVyIn8C0377N92G7z8siVLtwBMGNieymrFp3uzzJbiFcw0IBmAc0p2EnDRXE8pla+UKjeevgFc4eqx/sLCnacZmNzm4r4f7jBkCMyY4RlRPuaWfomIfGNMbRpBRASkpZmtwi3mbc8kJNDB2L51VJ1uDDU1sHatJUub9OsQRafosGbrxjLTgHwFdBeRziISDEwFFjjvICKJTk8nAPuMx8uA0SLS1lg8H21s8yuO5J0nPavIPf/vpdx4o+4VYsEy3nGtQxnaOZqFO083+8Qqj/LjH1u2qGBVdQ2Ld2cxsnc8kaEuJs7Wx7JlcP31sGKFZ8T5EBFhwoD2bDiST25R8wskMc2AKKWqgMfQP/z7gDlKqb0i8pyITDB2+6GI7BWRncAPgenGsQXA79BG6CvgOWObX7FoZxYiegTeZO65ByordStTCzJ+QHuOnilhr93u1jVyc+GVV+DgQbOVuMWmY4KwzxIAACAASURBVAWcOV/Brf098Nm/6SYd0j5nTtPPZQITBrZHKVi0q/nNwE3NA1FKLVFK9VBKdVVKvWBse0YptcB4/KRSqo9SaoBSaoRSar/Tsf9VSnUzbn43TFNKsWBnJkM6tXN/AdGZQYN0bazZs5t+LhMY1zeBQIewsIWUuW4yc+dCdbUeOFiQRbtOEx4cwIhecU0/WXAwTJ6sEynLrDeK7xYXSe/E1s3SjWVnonuJ/dnFHMkr4VZPuK9A1wW66y5YswayrZfZ3TY8mOu6x7BoZ5btxnKFWbOgTx/o189sJY2msrqGpXuyGZkaX3fTNHe46y4dzrzM7zzVLjFxYHt2nCrkZH7zqtBrGxAvsWjXaQIcwrimLiA68/DDsGqV7plgQcYPaE9mYSnbThaaLcW/OXUK1q/XiXQWZP3hMxReqHSt6rSr3Hij7ta5YEHD+/ohteugzW0GbhsQL6CUYuHOLK7pGk1MRIjnTpySAjfcoPuFWJBRqfGEBDpY2Ayn8h7l3Dld0vzOO81W4haLdmURGRLI8B4uVN51laAgPft+9VXPndOHdGjTioHJbVjczNZBbAPiBXZlnONkwQXPRF9dyokT8MQTkGW9D2JkaBAjesaxeHcWNXajqfrp21e7anr0MFtJo6moqmHZ3mxG9YknJNDDA50+fbQhsSi39EskPauI482o1bNtQLzA4t1ZBAUIY1I96L6qpaQEXnpJ98a2IDf3TySvuLzZl7l2m8JCS65x1bLuUB7FZVWM96T7ypk//1kPoCzIuH7692DJHusN/urDNiAeRinFkt1ZDOsWQ1SYF0ZLqal6JGbRkMYbe8UREuhgye7m8yXyKG+/DR066HUQC7JoVxZRrYIY1s2D7itnjh/Xbqzz571zfi+S1DaMgcltmtVn3zYgHmZPZhEZZ0u5ua8H4t/r4847dY+I09ZbS4gICeT6HrEs3WO7sepk7lztwkpObnhfP6O8qpqV6TmM6RNPcKCXflruvFPXxVq82Dvn9zK39EtkT2ZRs4nGsg2Ih1myJ4sAhzAqNd57F7njDl2d1KpurH6J5BSVs/2U7ca6iIwMHX1l0cKZ6w+fobi8inGeSJytj2uvhfh4y372a91Yi5vJLMQ2IB5EKcXS3Tr6qm14sPcu1Ls3XHedZUu839Q7juAAB4t3WdfX7xVqfxQtakCW7M4mMjSQYa70PXeXgACdVLhkCVyw3ig+qW0YA5qRG8s2IB5kX1Yxx/MvcLM3R2C1rFkDv/qV96/jBSJDgxjeI8Z2Y13Khx/q1rU9e5qtpNFUVtewIj2HUb296L6qZepUXeK90Jr5RLf0S2B35rlm4cayDYgHWbonC4fAaG+6r2oR0W4si85Cbu6XSNa5MnZmWPNHwCu8/75le4B/eSSfc6WVTa+86wrXX69L+rT3UqSXlxlnrI82h2gs24B4CKUUi3dnMbRLNNGeTB68HOPH634JFuSm3vEEBUizmcp7hORkuOoqs1W4xdI92YQHBzC8hw+rJBw8COXlDe/nZyS3C6N/UhSf7rG+C9c2IB7iYM55juaVeHcB8VL69YPPP4f8fN9d00NEtQriuu6xLNmdbdfGAnjmGcuW6aiuUSzfm82IXnGeq33VEKtXa1ffypW+uZ6HGdMngR2nCsk6Z71Oi87YBsRDLN2jS7eP6eMD91UtU6boiq0W/eEZ2yeBzMJSu8R7fj78/vewaZPZStxi87EC8ksqfLP2V8vVV0NUlGWjsWpdfcv3WrPLaC22AfEQn+7JJq1jW+IiPVC63VWuuELXx7Lol2hkajwOgWV7rT+VbxILFuiBgEXdkUv3ZBEa5OCGnj50X4WEaBfu/Pm6T47F6BobQfe4CJZafB3EVAMiImNF5ICIHBaRX9bx+hMiki4iu0RklYh0dHqtWkR2GDdTh+An8kvYn13MmD4+WEB0RkT/6KxYoUtdW4x24cFc1Tm6WfiCm8THH0PHjjB4sNlKGk1NjWLZ3myu7xFLWLCP+7ZPmaI7dK5e7dvreoixfRP07O289dZxajHNgIhIAPBPYByQCtwtIqmX7LYdSFNK9Qc+BF50eq1UKTXQuE3ARGpH0D43IACPPKLLXwR7Me/Ei4zpE8+h3PMcybNeaQqPUFwMy5frgYCI2WoazY6MQnKKyn0TfXUpY8ZAWBh88onvr+0BxvZNoEbByn3WdWOZOQMZAhxWSh1VSlUAs4GJzjsopT5XStUGS28Eknys0SWW7c0hNbE1ye3CfH/xPn10XHyoD11nHmS0YXRbrBvr1CmdGGpR99WyvdkEOoQbe/pw7a+WVq10SZMXXvD9tT2A/s1oZekZuJkGpAPgXDEuw9hWHw8DS52eh4rIFhHZKCKT6jtIRGYY+23Jy8trmuI6yC0qY9vJs+aMwGrJzIS//EXXCLIY7du0YkBSFMss/CVqEqmpsGOHLtFhMZRSLN+bw9Vdo71TONQVbrgB2rY159pNREQY2yeB9YfzKSqz3joOmGtA6pqv1xnPKSL3AWnAn502pyil0oB7gL+LSNe6jlVKva6USlNKpcV6oZPf8vQclDLJfVXLnj3ws59ZN6SxbwI7M85xutB6BrBJVFZa0ujXcij3PMfOlHw9izSNV16BN94wV4ObjO2bQEV1DZ/vzzVbiluYaUAyAOeSo0nAt8rLishI4NfABKXU16tNSqnTxv1RYDUwyJti62PZ3mw6x4TTIz7CjMtrRozQIY1W9QX3qQ1pbGGzkGXLdHviHTvMVuIWtbNGn1ReuBwLFug+IRbMJxqU3Ja4yBDLurHMNCBfAd1FpLOIBANTgYuiqURkEPAa2njkOm1vKyIhxuMYYBiQ7jPlBucuVPLlkXxG94lHzFwADQ6GW27RX6SqKvN0uEkXI6RxmcVj4hvNvHm6OGDqpbEj1mBZejaDUtoQ39rk9bfJk+HQIUj3+U9Ak3EYlbvXHMyjrLLabDmNxjQDopSqAh4DlgH7gDlKqb0i8pyI1EZV/RmIAOZeEq7bG9giIjuBz4E/KqV8/un57EAOVTXq6xG0qUyapBPS1q83W4lbjO2bwKZj+RSUVJgtxTfUJoDecoslI+hOFVxgT2aRua7bWiYasTcWnYGP7pPAhYpqNhw5Y7aURmNqHohSaolSqodSqqtS6gVj2zNKqQXG45FKqfhLw3WVUhuUUv2UUgOM+5lm6F++N4f41iEMSGpjxuUvZtw4iIjQ6yEWZHSqDmn8zKK+4EazYQPk5enRswVZnq5ni35hQNq3h6FDLWtAru4STWRIoCWz0u1MdDcpq6xmzcE8RqXG43D4Qfx+RATk5MD3v2+2Erfo26E1iVGhLWcdZN48PfMYO9ZsJW6xbG82PeIj6BwTbrYUze23Q+vWUFZmtpJGExzo4IZecazcl0O1xdob2AbETdYfPsOFimpGp/rBCKyWMCMPxYKLiSLC6NR41h7Ko7TCer7gRvPAA/DaaxAZabaSRlNQUsGW4wX+Mfuo5YkndGFRq+ZDpcZz5nwF205aq0unbUDcZPneHCJDAhnaJdpsKd9QVQU33QS/+53ZStxidJ8EyiprWHfI8/k6fseAATB9utkq3GLVvhxqFP41eKoNYikpMVeHm9zQM5bgAIflZuC2AXGD6hrFqv053NArzvvd1xpDYKDOLfjwQ7OVuMWQzu1oHRr4tX+92bJ4sS5fYlGWp+eQGBVK3w6tzZZyMf/5D8TE6PpYFiMyNIhrukUbeWXW8SD40a+fddh+8ixnzleYH/9eF5Mmwe7dcOyY2UoaTVCAg5t6x7NqXw5V1TVmy/EeTz8Nzz1ntgq3KK2oZt0hvfZnauh6XfTvr9dAFi82W4lbjE5N4ET+BQ7mWKcunG1A3GB5eg5BAeLb8tWuUhvSOH++uTrcZFRqPGcvVLLlhLV8wS5z4gRs364NvQVZdyiPssoa/3Jf1ZKWpiOyLPrZH5kah1isvUGDBkREholIuPH4PhH5m3NZ9ZaGrv+TzTVdY4gMNan+z+Xo2hX69rXsl2h4j1iCAx2WDGl0idr/i0UNyIr0HCJDA7mqSzuzpXwbhwMmTIBPP7VkNFZcZCiDktuwwkIuXFdmIP8GLojIAODnwAngHa+q8mMO557neP4FRvuy82BjeeIJy1Z3jQgJ5NpuMSxPb6atbufP15nn3bqZraTR6LW/XG7sFUdQgJ86LyZN0gvpq1aZrcQtRqUmsDvznGVa3bryKahS+ps8EXhZKfUyYL3YQw9Ru8A7srcfG5AHH4Qf/MBsFW4zOjWejLOl7MsqNluKZ6mogIMHv3EzWoytJ85SUFLhn+6rWkaM0JWpBwwwW4lbjDLWVVdaZBbiigEpFpEngfuAxUYjKD/03fiG5XuzGZDsB/V/GqKgANasMVuFW9zUOx4RLDWVd4ngYL0G8utfm63ELZbvzSY4wMH1/rj2V0twMPzkJ5Dkl62DGqRbXARdYsItE4noigG5CygHHlZKZaN7dvz58oc0T3KKytiZcc4/o68u5amn4OabLVkuPDYyRPuC91lnMdFlHA4I95Ps7UaglGJ5eg7XdIsmIsTHrWsbS2kpzJ4N+/aZrcQtRqXGs/GoNXqENGhAlFLZSqm/KaXWGc9PKqVa5BpI7YjYEgZk4kS4cMHSvuA9mUXNp0dIebnuHjl7ttlK3OJgznlOFlz42sXi11RU6Ez/N980W4lbjEqNp7JasfqA/yfU1mtAROQL475YRIqcbsUiUuQ7if7DivQcOkaH0S3OxN4frnLDDbpMhkWjsb72BVu4X/RFfPaZLjceFWW2ErdYka5ng3699ldLVJT+/Fv0sz8opS3R4cGWcOHWa0CUUtca95FKqdZOt0illJ+loHqf8+VVfHkkn1G9/TCBqi5CQrQLa8ECXTrcYtT6gq3wJXKJ+fN1wcsbbzRbiVusSM+xxtpfLRMn6oCFAwfMVtJoAhzCTb3jWL0/l4oq/06odSUPZGQd26Z5R47/suZAHhXVNdaYwtcycSLk5urENQtiJV/wZamp0YZ87Fht2C2Gpdb+ahk/Xt9bdBYyKjWB4vIqNh3LN1vKZXFlEf0ZEfm3iISLSLyILATGe1uYv7EiPZu2YUFc0bGt2VJcZ/x4PQpLSzNbiVtYyRd8WbZsgawsy4bv1roRLTV4SkmBQYMsO3i6tlsMoUEOv5+Bu2JArgeOADuAL4D3lVK3e+LiIjJWRA6IyGER+WUdr4eIyAfG65tEpJPTa08a2w+IyBhP6KmPyuoaPtufy4294gn01wSquoiIgO7dzVbhNlbyBV+WiAh46CHtUrQgtWt/3a2w9ufMZ5/BrFlmq3CLVsEBXNc9lpV+XlzRlV/DtsBVaCNSDnQUDywCGPkk/wTGAanA3SJyaXPoh4GzSqluwEvAn4xjU9E91PsAY4F/GefzCl8dK6CorMpaI7BaDh6EqVN1z2iLYSVf8GVJTYWZM6GdH5b/aIDz5VVsOJzPSKus/TnTxg86hTaBUanxnD5Xxt7T/huz5IoB2QgsVUqNBa4E2gOeaLw9BDislDqqlKoAZqOz3Z2ZCLxtPP4QuMkwXhOB2UqpcqXUMeCwcT6vsDw9h5BAB8N7xHjrEt4jNBQ++EB3wLMgVvEF10tuLuzYYckmXwBrD1pw7c+Zn/8cpllzyfamXnE4BL9OKnTFgIxUSv0XQClVqpT6IfAtd5MbdABOOT3PMLbVuY9Sqgo4B0S7eCwAIjJDRLaIyJa8PPd86ZXVNYzsHU9YsJ8nUNVFSgoMHGjZxUSr+ILr5b33tC/+xAmzlbjFivQc2oQFkWaltT9nyspgzhxLNpqKjgjhio5t/fqz70oi4UkRaSsiQ0RkuIgM99C165oPXzpMq28fV47VG5V6XSmVppRKi411rwTDC5P78co9g9w61i+YOBE2bNCjYYthFV9wvcyfD/36QadOZitpNFVfr/3FWWvtz5mJE7URWbHCbCVuMSo1nn1ZRZwquGC2lDpxJYz3EWAtsAz4rXH/Gw9cOwNIdnqeBJyubx8RCQSigAIXj/UolvP/OjNxonahLFpkthK3GNXb/33BdXLmDHzxhWWjrzYfL+BcaaW1wncvZfhwvRZi0Rn4KKNw5So/Tah1ZVjxI/Taxwml1AhgEOCJuMqvgO4i0llEgtGL4gsu2WcBUOvAvB34zKgMvACYakRpdQa6A5s9oKl5MnCg7pUeaEEXHHBj7zhrFldcvFjngFjUgKxIzyE40MF13f24eGJDBAXp6LdFiyyZUNs5JpxucRGs8FMD4sovSplSqkxEEJEQpdR+EenZ1AsrpapE5DH0jCYA+K9Saq+IPAdsUUotAGYC74rIYfTMY6px7F4RmQOkA1XA95VS1vt0+AoRWLnSbBVuExMRwhUp2hf8+KgeZstxnUWLoEMHuOIKs5U0GqUUK9JzuLZbDOH+XjyxIR54ABIS9DpIa+sV0RiVGs8ba49yrrSSqFb+VQjdlRlIhoi0AeYBK0RkPh5yFymlliileiiluiqlXjC2PWMYD5RSZUqpO5RS3ZRSQ5RSR52OfcE4rqdSaqkn9DR7Kish35rRTKNS40nPKiLjrH/6guvkrbf0LMSC7s/92cVknC21bvSVM2PGwF//aknjAfqzX1WjWH3A/9YwXVlEn6yUKlRK/QZ4Gj0rsGY/zpaMUtCjhw5rtCBWa7QD6LLtFm1stCI9BxG4qXec2VI8Q1UVrF9vyXDqgUltiI0M8ctw3kaFViil1iilFhh5GzZWQgSuuQYWLrSkL7hLbARdY8P91hf8Lf70J/j7381W4TYr0nMYmNyGuEiLFE9siDffhGuvtWSPEIdDGNk7jjUH8iiv8q/vrkVj82zcYuJEyMuDL780W4lbjEpNYNPRAs5d8PPiitXV8Le/wcaNZitxi6xzpezOPNc83Fe13HKLvrdoNNbo1ATOl1ex8WiB2VIuwjYgLYmxY3VUimWz0g1f8EH/8wVfxKZNOufGotFXK63UOM1V2reHK6+0rAG5ums0YcEBLN/rX106XckDeUxELJqGanMRrVvrfhTz5lnSFzwouQ0xESEs3+vnbqz583XI9LhxZitxi+XpOXSOCadrrMWKJzbExInauGdlma2k0YQGBXBDz1hW7suhpsZ/vruuzEASgK9EZI5RPdd6ISU23/DMM5Zt9elwCKNS41l9INfvfMEXMW8ejBhhyWJ+RWWVbDyaz6hUCxZPbIjaGaFVE2pT48kpKmdX5jmzpXyNK1FYT6ET9WYC04FDIvJ7EenqZW023uCaa+C66ywZWgrarVJSUc2GI34ajnz+PHTtCnfcYbYSt1h9II/KatW81j9q6dMH1q6F6dPNVuIWI3rGEeCQr9sL+wMurYEY2d/Zxq0KXeL9QxF50YvabLzF1q06Lt6CXN01mvDgAP91Y0VEwJIl8OijZitxi+V7s4mJCGZwSjP0WovowVOQfyXjuUqbsGCu6tzOrz77rqyB/FBEtgIvosu491NK/R9wBTDFy/psvMGnn8JPfwqnvVo+zCuEBgVwvR/6gr+mwL+iZBpDeVU1qw/kMbJ3PAEOa85QG6S4GH72M/0dsCCjUuM5lHueY2f8o7qwKzOQGOA2pdQYpdRcpVQlgFKqBrjVq+psvMMkIw90waWlx6zB6NQE8orL2ZFRaLaUi8nOhrg4+M9/zFbiFl8eyed8eRWj+zRD91UtYWG6QsA775itxC1qXYv+4sZyZQ3kGaVUnc0MlFLWy8qx0R3yunWDTz4xW4lbjOgZR6BD/GoqD2iDXF0NQ7zW28yrLE/PISw4gGu6WrBxmqsEBMCECbrETIX18qGT2oaRmtjabz77dh5IS0QEJk/WPaML/WwU7wJRYUFc1aUdy/1kFPY18+ZBly66/4fFqKnRxRNv6BlLaJDXukP7B5MmQVERrF5tthK3GN0nnq0nz5JXXG62FNuAtFgmTYLISEuWdgDtxjqaV8Lh3PNmS9EUFcGqVdowWzDCbUdGIXnF5Yw2+k80a0aO1HXKLJpQO6ZPAkrBSj8o62MbkJbK0KE6W/rqq81W4ha1vuBl/pKZu2SJdolMnmy2ErdYvjeHQIcwomczKZ54OVq1grvugpAQs5W4Ra+ESJLbtfKLz75tQFoqDofOllZKNz2yGO3btKJ/UpT/lHYYMQJee00bZguyPD2boV2iiQqzZohro5k5E156yWwVbiEijElNYMPhfIrLzK0LZxuQlsyhQ9C9Oyy1ZjuVMX0S2JlxjqxzpWZLgfh4mDFDL9JajMO5xRzNK2ne0Vf1YcE1QIAxfROoqK7h8wOeaA7rPqYYEBFpJyIrROSQcf+trCURGSgiX4rIXhHZJSJ3Ob32logcE5Edxm2gb/+CZkLHjro6r4V9wYD5ESmbN+sRbVmZuTrcZJnx/jXL7PPL8dBDlp0xDk5pS0xEsOkzcLNmIL8EVimlugOrjOeXcgF4QCnVBxgL/N3ojFjLz5RSA43bDu9LboYEB+t+0fPnW7JHSLc43SPEdF/wG2/A449bcvEc4NM92QxMbkNiVCuzpfiWtDQ4cMCSgSQBX9eFM7dHiFkGZCLwtvH4berocKiUOqiUOmQ8Pg3kArE+U9hSmDxZz0LWrzdbiVuM7ZvApmMFnC0xKaa/ulob4FtuseSibMbZC+zOPMfYvi0g+upSaosrfvyxuTrcZHQf3SNkw2Hz6sKZZUDilVJZAMb9ZUM/RGQIEAwccdr8guHaeklE6v3misgMEdkiIlvy8sz1F/ol48bpHz6LJhWO6ZNAdY0yL6RxwwZtgCdZs8tzrfuq1h3YoujQQbuwLGpArukaTURIoKkzcK8ZEBFZKSJ76rg1qsuOiCQC7wIPGuVTAJ4EegFXAu2AX9R3vFLqdaVUmlIqLTbWnsB8i8hIeP55GDPGbCVu0a9DFO2jQr/+IfQ5n3yiXYEW7f2xbG82vRIi6RwTbrYUc7jtNti2DY4fN1tJowkJ1D1CVqTnUG1SXbhAb51YKTWyvtdEJEdEEpVSWYaBqLPFnIi0BhYDTymlvu4PWjt7AcpF5E3gpx6U3vL4qXXfPhFhdJ8EZm0+yYWKKsKCvfaRrpujR7Xxbd3at9f1AHnF5Xx1vIAf3tjdbCnmMXWqnonEWLN8y9i+CSzalcVXxwsY2iXa59c3y4W1AJhmPJ4GfKvPpIgEA58A7yil5l7yWqJxL+j1kz1eVdsSOHjQsqUdxvRJoLyqhtVmhDTOmwdz5za8nx+ycl8OStEy1z9qSU6Ge+7RZfgtyIiecYQEOvh0jzluLLMMyB+BUSJyCBhlPEdE0kSktpTpncBwYHod4br/E5HdwG50teDnfSu/GfL97+s8Bgu2ur2yU1uiw4NZ6usvUW0CpgUXz0FHX3WMDqNXQqTZUswlN1f3x8kxvzRIYwkPCeT6HrF8uifblPYGphgQpVS+UuompVR3477A2L5FKfWI8fg9pVSQU6ju1+G6SqkblVL9lFJ9lVL3KaX8pCCShZkyRScW7t1rtpJGExjgYHSfBD7bl0NZpY9CGpWCwYP1+pEFOVdayYYjZxjbJ6H5ta5tLLm52o1r0Xyocf0SyC4qY/sp3ydF2pnoNppJk3Qeg0UjUsb1TaCkopp1h8745oK7d8POnWDRwIzP9udQWa0Y3RKjry6lTx9dkeGjj8xW4hY39oonKED4dE9Wwzt7GNuA2GgSEmDYMMt+ia7uGk1UqyCW7vbRl+ijj7TBtWj47pLd2SS0DmVQcpuGd27uiOgZ+GefQb55ORXuEtUqiGHdYli6JxvlYxe0bUBsvuG223RWbpbvRzJNJSjAwejUeFbsy/FNZu5HH+n+2vHWK/9RXFbJmoN5jOuXgKO5tq5tLHfcoZNCLerGurlvIhlnS9l7usin17UNiM03PPSQXkhMTDRbiVuM65dAcZkPMnMPHNBrRVOmePc6XuKz/blUVNVwSz9r/p+9wqBB0KMHHDnS8L5+yKhU3cd+qY/dWLYBsfmGqCho+626lpZhWLcYIkMCWeJtN1ZEBDz1lJ6xWZAlu7OIbx3C4BTr/q89johe1/r9781W4hZtw4MZ2qWdz91YtgGxuZjt2+Haa+HwYbOVNJqQwABGpsazPD2Hymov9jjp0AF+9ztISvLeNbxESXkVqw/kMa5vou2+upTgYH1vwcKiAOP6JnI0r4QDOcU+u6ZtQGwuJjpaF1a0aHLc2L4JnCut5MsjXnJjnTwJixfr7oMWZNX+XMqrarjZdl/VzYMPWrar5Ni+CTgEFu30nRvLNiA2F5OSotvczpljthK3uL5HLOHBASze5aUv0dtvw6236gKKFmTJriziIkNI62i7r+okNhY+/RTOnjVbSaOJiQjhmq4xLNp12mduLNuA2HybO+6AHTt0YqHFCA0KYFRqPJ/uzaaiygturDlztIuvQwfPn9vLlJRX8fmBXMb1taOv6uWOO6CyUpfotyC39E/keP4Fn0Vj2QbE5tvcfru+t6gb69b+7TlXWskXhz08S0hPhz174K67Gt7XD/n8gHZfjbPdV/WTlgadOln2sz+2TwIBDmGRt2bgl2AbEJtvk5wMjz2ms3MtyHU9YmgdGuh5X/CcOd8knVmQhTtPExcZwpWd2pktxX8R0bOQFSugoMBsNY2mbXgww7rFsHi3b9xYPq59bWMZ/vEPsxW4TUhgAGP6JLB0TzZlldWEBgV45sRffAHDh1syT6aorJLPD+Rx31UdCbDdV5dn2jRdmSHAQ58bH3Nr/0R+/uEudmWcY4CXKw3YMxCb+snP1812LMj4Ae05b4Sseozlyy3r2li2R68JjR9gPePnc/r0gSee0HlRFmRMagJBAcKiXae9fi3bgNjUz1136V4JFizxfk3XaNqFB3v2S+RwWLZ44sJdWaS0C2OgXfvKNUpK4L33INu8drHuEhUWxHXdY1m8K8vrJd5tA2JTP3fdpct2bN9utpJGExjgYGzfBFbty+VCRVXTTqaULjT56queEedj8s+XtTkJSQAAH2hJREFUs/7wGcYPSLRLt7vKqVNw//0we7bZStzi1v6JnD5XxraT3g1HNsWAiEg7EVkhIoeM+zqD0kWk2qmZ1AKn7Z1FZJNx/AdG90IbTzNlCgQFwaxZZitxi/H921NaWc2qfXV2THadTZtgwwYIDfWMMB+zZHcW1TWK8QPamy3FOvTqBQMHWvazP7pPAqFBDubtyPTqdcyagfwSWKWU6g6sMp7XRalTM6kJTtv/BLxkHH8WeNi7clso7drB2LH6S1TjxdIgXmJI53bEtw5h/o4murH+9z9tPCxa+2rhzix6xEfQK8F6fdtN5e67YfNm3ffeYkSEBDIqNYHFu7K8WtbHLAMyEXjbePw2uq+5Sxh90G8EPnTneJtGcs89kJlpycX0AIcwcWAHVh/IpaDEzdIjlZXwwQcwfjy0tt4P8OnCUjYfL2CCPftoPFOn6nuLurEmDWzP2QuVrD3ovaoJZhmQeKVUFoBxH1fPfqEiskVENopIrZGIBgqVUrWO7QzAemnBVmHCBF1YMS3NbCVuMWlgB6pqFIvdXUxfuVKXLbn3Xs8K8xELd+q/+9b+tgFpNCkpeu3LgmuAAMN7xNI2LIh5TZ2BXwav5YGIyEqgrn6Zv27EaVKUUqdFpAvwmYjsBurK0a831EBEZgAzAFJSUhpxaRsAwsKga1ezVbhN78RIesZH8sn2TO6/ulPjT5CUBP/3fzBunMe1eRulFB9vy2RQShs6xYSbLceaLFliyZkn6CZrt/RP5MOtGZwvryIixPM/916bgSilRiql+tZxmw/kiEgigHFf5yqnUuq0cX8UWA0MAs4AbUSk9t1IAuo1sUqp15VSaUqptFiLhmCaTk6OLm+ydKnZShqNiDB5cAe2nSzkRH5J40/Qrx/861/flPq2EOlZRRzIKea2wdYrO+831BoPi5Z4nzSwA2WVNSzf651wZLNcWAuAacbjacC3KpeJSFsRCTEexwDDgHSl8/M/B26/3PE2HqRdO1i7FmbONFuJW0wY0B4RmLe9kVP5rVv1zYJ5MAAfb8skKEAY399OHmwSr7wCPXtCVRPDwU3gio5tSWrbymtuLLMMyB+BUSJyCBhlPEdE0kTkP8Y+vYEtIrITbTD+qJRKN177BfCEiBxGr4lY85fNKgQFwX33wYIFOjvdYrRv04qhnaOZtyOzcfWBnnpK94awoAGpqq5h/o5MbuoVT5sw682e/IrkZN3qdvlys5U0GhFh4sD2fHEoj7zico+f3xQDopTKV0rdpJTqbtwXGNu3KKUeMR5vUEr1U0oNMO5nOh1/VCk1RCnVTSl1h1LK8++MzcVMm6YjkiwaFz95cAeOnSlhZ8Y51w7IzNQ/GA88oDPQLca6Q2c4c76C2wbb8SVNZtw43WjtnXfMVuIWkwd1YMrgJMqrPO+Gs943w8YcBgzQiVVvv93wvn7IuL4JhAQ6+GhrhmsHvPOOzn2ZPt2rurzFR9syaBsWxA096wtwtHGZ4GCdEzJvHhQWmq2m0XSLi+TPdwwgqW2Yx89tGxAb13niCbj5Zkv6giNDgxjbN4H5OzIpq2xgJKYUvPUWXHcddOvmE32e5FxpJcvTc5gwoD3BgfZX3CNMmwbl5ZYtpukt7E+Xjevcfz/89rcQaM0uAHelJVNUVsWyhiJSjh+HrCzdH9uCLNmdRUVVjR195UmuuAJeeEEPKmy+xjYgNo2jshIWLrTkLGRol2iS27Xig69OXX7Hzp21Abn7bt8I8zAffHWKbnER9E+yZjlyv0QEfvUrXSPL5mtsA2LTOJYs0dnpy5aZraTROBzCnVcks+FIPifzL9S9U02NdmGFh1uyeOL+7CJ2nCrk7iEpduVdb7BmDbz/vtkq/AbbgNg0jnHjIC4O3njDbCVucXtaEg6BuVvrmYX87386eTDLNz2lPc3szacIDnBw2yA7+sorvPwy/PjHUOFmbbVmhm1AbBpHcLBeG1i0SIe6WozEqFYM7xHLh1szqK6r2c7MmVBWpluaWoyyymo+3pbB2L4JtA23cz+8wowZujbafDt3GWwDYuMOjz6qSztYNDP9zrRkss6Vse7QJVVK09O1i+LRR7XP22Is2Z1FUVkVU4ckmy2l+TJqlC6y+PrrZivxC2wDYtN4unbVX6TVq81W4hYje8fTLjyYWZtPXvzCq6/qGdZDD5kjrInM3nyKTtFhXN0l2mwpzZeAAHjkEV2l+cgRs9WYjm1AbNxj1iz9JbIgwYEO7kxLZkV6DqcLS/XGkhKdJHn77Zbse3449zybjxcw1V489z4PPgjt2+s2By0c24DYuEd0tC7xYcFwXoB7r9Kl/d/fZMxCgoL0DORnPzNRlfvM3nySQIcwxc798D5JSbpn+pgxZisxHduA2LjP0qV6JHbyZMP7+hnJ7cK4sVc8szaf1DWCastVDBxotrRGU1JexQdbTjG2bwKxkSFmy2kZOBx6HfC095o1WQHbgNi4T2oqnDkDr71mthK3eODqjuSXVLBh7kr4wx+gqK5eZf7Px9szKS6r4sFhnc2W0rIYO1a7PFswtgGxcZ+OHWHiRG1ALtSTmOfHXNsthi4x4aiXX4bf/96SkVc1NYq31h+jf1IUg1PamC2nZTF+PHz5JWzebLYS07ANiE3TePxx3SPk3XfNVtJoHA7h0e5hDNuygvzb7oLISLMlNZp1h89wJK+EB4d1shfPfc306foz8/LLZisxDduA2DSN667TheZeekmXAbEYkzd8QlB1NW9cMcFsKW7x1vpjxEaGcEu/9mZLaXn8//buPK6qam3g+O9BMDD1mmiW4ZCFEwioVFY3MrXSey313gYrzQZv5ptRmaU26Wta3gY1Pw1mKWZZ17Te9FpplhZdFHMKckrNQEkzxBklGZ73j7XlIjF5POdshvX9fM4Hzh6fjchz1tp7Pat+fbj3Xvjwwxp7L8SVsqoi0hCYC7QE0oBbVPVAsW2uASYXWdQW6K+qn4jILOBq4OTsQHep6veexJKbm0tGRgY5OTme7F4tBAcHExYWRlBQ0OnvLGKSR1BQ1Zt4KTub4LfeZFOXbszYG8hdh3I4709Vp/7VjsyjLP8xk4d7hNuy7W558EHTAnnvPXj8cbej8Tu36nKPAr5S1YkiMsp5P7LoBqq6HIiBwoSzHSg6p+Rjqjr/TAPJyMigXr16tGxZM7sAVJWsrCwyMjK48EIPb8JW1RLXWVlw2WWExo+g4NvfmfGfHTz51/ZuR1Vhs1akUbtWAHdc1sLtUGquVq3MfZDYWLcjcYVbH1v6ACentnsH6FvO9jcBn6uq1+/U5uTkEBoaWiOTB5g5k0NDQ8+8Bfbrr6YESGqqdwLzh+bN4fPPadKrO72jzuf9VTs5dCzX7agqJPPI78xdvYs+MU3to7tuu+wyM0JdS6itVs25lUCaqOoeAOdrefNu9geKT8Y9QURSRWSyiJT6P0hE7hORNSKyJjMzs7RtTiP06scr11+7tilz/eKLZ34sf1i/HtLTC98OibuI7BP5vJuc5l5Mp2HGf34mN7+AoV0vcjsUC8wUyJ061bgqvT5LICLypYhsKOHV5zSPcz7QASg6AcVozD2RS4CGFOv+KkpVp6tqrKrGNq6CJSqqjIYNYehQk0S2bnU7mrKpwv33m+f4nU+N7ZvWp2ubxiQkpZU/5a3LDh47wbsr0+gd1ZRWjeu6HY4FpvzN99+beyE1iM8SiKr2UNXIEl4LgL1OYjiZIH4r41C3AP+nqoV9C6q6R43fgQTgUl9dhxsGDx7Mpk2byt1uypQpzJ49u8xt+vfvz7Zt27wVWtkeewzOOgvGj/fP+Tz1+efm2f1HHjll7Mf9V19EVvYJ5q3NcDG48iUkpZF9Ip8Hrql687VXWz17mqcRn3++ypb38YRbXVgLgUHO94OAsorr30ax7qsiyUcw9082+CBG17z99tu0b1/2zdy8vDxmzpzJ7bffXuZ2Q4cO5YUXXvBmeKVr0sS0QubMAX8lrdOlCs88Y6atLTbn+WUXNqRj8wZM+/onU96kEjqSk8usFWlc174Jbc6reuNWqi0ReOopU2Bx7ly3o/Ebt57Cmgh8KCL3AjuBmwFEJBa4X1UHO+9bAs2Ab4rtP0dEGgMCfA/c742g/vffG9m027vlLNo3rc+YGyJKXZ+dnc0tt9xCRkYG+fn5PP3007zxxhu89NJLxMbGUrduXR566CEWLVpESEgICxYsoEmTJixbtoxOnToRGBhIXl4el19+OS+++CJdu3Zl9OjRBAQEMGHCBK666iruuusu8vLyCAz0wz/3Y4+ZCZnOPtv35/LEggWwdi0kJJhHj4sQER69tg0DZqxiTvJO7vlz5SsN8l7yTg4dz2VYN9v6qHRuvBEiI2HCBFNXrao91u4BV65QVbNUtbuqhjtf9zvL15xMHs77NFW9QFULiu3fTVU7OF1iA1T1qL+vwVsWL15M06ZNSUlJYcOGDfTs2fOU9dnZ2XTp0oWUlBTi4uJ4y5lKNikpic6dOwMQGBjIrFmzGDp0KEuXLmXx4sWMGTMGgICAAC6++GJSUlL8c0HnnQevvWaKLFZGmzdDRAQMGFDi6j+HN+KKi0J5bfl2jv5euboiDh3P5c3En7i6dWOiwmzZkkonIMD87s+YUSOSB7jXAqmUymop+EqHDh0YMWIEI0eOpHfv3lxVbExF7dq16d27NwCdO3dm6dKlAOzZs4d27doVbhcREcHAgQO54YYbWLlyJbVr/3dK03PPPZfdu3cXJhy/WLkSkpJgxAj/nbMiRo+GRx+FMlpjj13fhn6vr2Dmf34mvnu4H4Mr2+vLt3PoeC4je7Z1OxSrNHFxbkfgVzUjTVZirVu3Zu3atXTo0IHRo0czbty4U9YHBQUVPmZbq1Yt8pwbdCEhIX8Yu/HDDz/QoEED9u7de8rynJwcQkJCfHgVJVi40HRnrVrl3/OWJjcXkpPN97XLni+8Y/NzuD6iCW8l7mB/duV4LHPX/mMkJKXx905htG9a3+1wrLLk5pq5019+2e1IfM4mEJft3r2bOnXqMGDAAEaMGMG6desqtF+7du3YXmRGtI8//pisrCwSExOJj4/n4MGDheu2bt1KRISfW1dPPGG6sx5+uHIMsHrtNbj8cnP/owJGXNeG7BN5vPF15Zh17qUvfiQgAB69rrXboVjlCQqCPXtg3DgoZeyZX+3cCUd908tvE4jLfvjhBy699FJiYmKYMGECTz31VIX269WrF4mJiQDs27ePUaNGMWPGDFq3bs2wYcN46KGHANi7dy8hISGcf/75PruGEtWrZ0qkJyebsSFu+vVXGDMGevUyg70qILxJPf7eKYxZK9LY/tsRHwdYtpRdB1nw/W4G/7kV5//Jzy1JyzMvvGCmSS7Wo+B3BQXmhv7VV/vmg5yq1phX586dtbhNmzb9YVlV0bdvX926dWuZ20yaNEnffvvtco/lk59Dfr5q586qF1ygevSo949fUYMGqdaurVrOz6q4zCM5GjV2id4ybYUWFBT4JrZy5OcX6E1vJGnnZ7/QIzm5rsRgeej++1Vr1VJ182/MW2+pgmpCwhkdBlijJfxNtS2QKmzixIns2bOnzG0aNGjAoEGDytzGZwICYOpU+J//MbWC3LBiBbzzjrlxHn56N8Qb1T2LUb3asurn/Xy07hcfBVi2Od/tZHXaAR6/vi11z7LPvFQpY8ealvjw4e6cX9WMjI+LAx/9DbC/kVVYmzZtaNOmTZnb3F1ssJzfXXGFebll1y7z2O6TT3q0+62xzZi/NoPnPttM97bncs7ZZd+A96aMA8eY+NlmrgpvxM2xYX47r+UlTZrAvHnQ2qX7ViKwdCns3++z2TZtC8Tyjy++gB49zCBDf7r1VlMh2MOBjQEBwoR+kRw+nstzn232cnClU1VGf/wDAM//rUONL/hZZfXoYao+q8KhQ+Vv7y2bNpnzBQWZROYjNoFY/lFQAF99Zco9+MO338LMmeY/7hkO6mp7Xn3+EdeKeWsz+DS17C5Db5m3NoNvt+1jVK+2hJ1Txy/ntHzo1luhTx//zNqZmQnXXWdGxvv4CUibQCz/6NnTVMCdNAm+KV6ZxssOHIA77jCF7bzU4nmkR2s6Nm/AyI9S+XlftleOWZrtvx3l2X9v4tILG9rJoqqL6683v/e+HhtSUAB33gn79sGUKT7rujrJJhDLf1580czgdscd8IuPbkqrwpAh5jn8998HLw2grB0YwKu3dyKwlvDAnHU+K/l+6Hgu/5i9hrOCAph8awwBAbbrqlq45x646SYYNcpUg/aVF16AxYtN8ujY0XfncdgEUg2sX7+ewYMHl7nNq6++SkJCgp8iKkXduvDRR6ZvdsYM35xj7Fhz43L8eLjkEq8e+oIGIUy+JYZNew4zduFG1MvdA/kFSvwH68k4cIw3BnTmggZ2zEe1IQKzZkFUFPTvD1u2eP8cSUmmi/jWW82HKD+wCaQaeO6553jwwQfL3Oaee+5h6tSpfoqoDNHRsG4dPP2094+dmmoGbt1zDzz+uPePD1zT9lweuOYi/rV6Fy998aNXk8g/F2/hm62ZjOsTySUtG3rtuFYlcfbZphp0q1a+GRnerp1JHtOn+7zr6iSbQIrr2vWPr9dfN+uOHSt5/axZZv2+fX9cVwGzZ88mKiqK6OhoBg4cSHp6Ot27dycqKoru3buzc+dOAObNm0dkZCTR0dHEOUXbjhw5QmpqKtHR0QDEx8cX1tNasmQJcXFxFBQUUKdOHVq2bMl33313Jj8d7wgPN7/g27aZuTnyvdQdFBVlbtS/+aZP/wM9em0bbru0Ga8t/4kpX575vCeqyvOfbWZ64g4GdmnBbZc290KUVqXUvLn5ABUba7pbi5Qc8tiWLfD772ZW0DlzoL7/aqXZBOKyjRs3MmHCBJYtW0ZKSgqvvPIKw4YN48477yQ1NZU77riD+Ph4AMaNG8eSJUtISUlh4cKFAKxZs4bIyMjC402cOJG5c+eyfPly4uPjSUhIIMB5Cik2NpZvv/3W/xdZmvnz4dlnzdMpRzwsF6IK06bBp5+a9926lVlp1xsCAoQJfTtwc+cwXvlqG698uc3jlkhefgEjP0rlzcQdDOjSnLE3+r8itOVnJz/cjB9vZjH86SfPj7V2rRlnNWyYd2I7XSUNT6+ur8pYymTq1Kn6xBNPnLIsNDRUT5w4oaqqJ06c0NDQUFVVHTJkiPbo0UOnT5+u+/btU1XVOXPm6JAhQ07ZPykpSWvVqqVTp049Zfn06dN1+PDhJcbh2s/h9ddNuYcOHVTT0k5v3+xsU6YEVG+7zSfhlSUvv0AfmbteW4xcpPfOWq2ZR3JOa/+so7/r4HdWa4uRi/TlL350rVyK5ZKVK1UbNlRt0kQ1Ken09s3PV500yZToadZMdccO38TooDKVMhGRm0Vko4gUOLMQlrZdTxH5UUS2i8ioIssvFJFVIrJNROaKiP+GB3uZqpY7SOzk+mnTpjF+/Hh27dpFTEwMWVlZpZZ1Dw0NZffu3acsd6Wse3mGDoXPPoP0dIiJqVi1XFXTVXXFFTB7timU+N57vo+1mFoBwks3RfN07/Ykbsuk55RElm7aW25rJL9AeS85nWte+prlW35j7A3tGX5taztYsKbp0sXc+A4JgSuvNFMsF5uKoUT795vCoMOHm8fj1641UzS7oaSs4usX0A5oA3wNxJayTS3gJ6AVUBtIAdo76z4E+jvfTwOGVuS8lbEFsmHDBg0PDy9sUWRlZekNN9ygs2fPVlXVhIQE7du3r6qqbt++vXC/mJgYXb9+vW7evFmvvPLKwuVpaWkaHh6uv/zyi0ZHR2tycnLhumHDhukHH3xQYhxu/xx0yxbVu+9WzXE+xa9erZqcrHrsmHmfm6t66JD5PjnZtDrOO0/100/dibeYLXsO6/WTv9EWIxdp95e/1je+3q57Dh4vbFUUFBTohl8O6uvLt2uvKYnaYuQi7f/mSt3662GXI7dcd+SI6siRqiEhqqmpZll6ulmuqlpQYIqRnmyhHzyo2qiR6rRpZp0fUEoLxNUupXISyOXAkiLvRzsvAfYBgSVtV9arMiYQVdVZs2ZpRESERkVF6aBBg/Tnn3/Wa665Rjt06KDdunXT9PR0VVXt16+fRkZGakREhMbHxxf+cYqMjNTDhw9rQUGBdu/eXRcsWKCqqmvWrNHIyEg9fvy4qqp27NhRMzMzS4yhMvwcCuXkmAq+YLq3wsJUAwNV+/c36wsKVOfPV3Wuq7LIyc3TD1al699eT9IWIxdpi5GLtOWoRRrxzGKNHLO4cNn1k7/RT9Zn2C4r61TOh0hVVb3xRvM737ixalCQ+b/QqdN/1x886NfQSksgouU0t31JRL4GRqjqmhLW3QT0VGeOdBEZCFwGjAWSVfViZ3kz4HNVjSx+DGf9fcB9AM2bN++cnp5+yvrNmzefMjVsVTR58mTq1atX5liQ9evXM2nSJN59990S11e6n8POnaZpvm6d6d4KCzNN/htvdDuyCtmReZRlW37j8PFcjv6ez4n8fGKanUNceCPOrR/sdnhWZZeYaAYcHjgA55xjXhER8Je/+O0R3aJEZK2q/uF2g88eVxGRL4HzSlj1pKouqMghSlimZSwvkapOB6YDxMbGVoKp8bxv6NChzJs3r8xt9u3bx7PPPuuniLygeXPz6tfP7Ug80qpxXVo1rut2GFZVFRdXJeZX91kCUdUeZ3iIDKBZkfdhwG5M91UDEQlU1bwiy2us4OBgBg4cWOY21157rZ+isSyrpqjM40BWA+HOE1e1gf7AQqc/bjlwk7PdIKAiLZpSudmNVxnU9Ou3LMszbj3G209EMjA3wD8VkSXO8qYi8hmA07oYBiwBNgMfqupG5xAjgeEish0IBTwurBQcHExWVlaN/SOqqmRlZREcbPvlLcs6Pa7eRPe32NhYXbPm1Pv1ubm5ZGRk/GEsRU0SHBxMWFgYQUFBbodiWVYl5Peb6FVFUFAQF7o1CMeyLKsKq8z3QCzLsqxKzCYQy7IsyyM2gViWZVkeqVE30UUkE0gvd8OSNcKMQalJ7DXXDPaaq78zvd4Wqtq4+MIalUDOhIisKekphOrMXnPNYK+5+vPV9douLMuyLMsjNoFYlmVZHrEJpOKmux2AC+w11wz2mqs/n1yvvQdiWZZlecS2QCzLsiyP2ARiWZZlecQmkAoQkZ4i8qOIbBeRUW7H40si0kxElovIZhHZKCIPuR2Tv4hILRFZLyKL3I7FH0SkgYjMF5Etzr/35W7H5Gsi8ojze71BRD4QkWpXhlpEZorIbyKyociyhiKyVES2OV/P8ca5bAIph4jUAl4DegHtgdtEpL27UflUHvCoqrYDugAPVPPrLeohzNQBNcUrwGJVbQtEU82vXUQuAOKBWGcK7FqYeYaqm1lAz2LLRgFfqWo48JXz/ozZBFK+S4HtqrpDVU8A/wL6uByTz6jqHlVd53x/BPNH5QJ3o/I9EQkD/gq87XYs/iAi9YE4nLl0VPWEqh50Nyq/CARCRCQQqEM1nM1UVROB/cUW9wHecb5/B+jrjXPZBFK+C4BdRd5nUAP+oAKISEugI7DK3Uj8YgrwOFDgdiB+0grIBBKcbru3ReRst4PyJVX9BXgJ2AnsAQ6p6hfuRuU3TVR1D5gPicC53jioTSDlkxKWVftnn0WkLvAR8LCqHnY7Hl8Skd7Ab6q61u1Y/CgQ6AS8oaodgWy81K1RWTn9/n2AC4GmwNkiMsDdqKo2m0DKlwE0K/I+jGrY7C1KRIIwyWOOqn7sdjx+cCVwo4ikYboou4nIe+6G5HMZQIaqnmxdzscklOqsB/Czqmaqai7wMXCFyzH5y14ROR/A+fqbNw5qE0j5VgPhInKhiNTG3HRb6HJMPiMigukX36yqk9yOxx9UdbSqhqlqS8y/7zJVrdafTFX1V2CXiLRxFnUHNrkYkj/sBLqISB3n97w71fzBgSIWAoOc7wcBC7xx0Bo/pW15VDVPRIYBSzBPbcxU1Y0uh+VLVwIDgR9E5Htn2ROq+pmLMVm+8SAwx/lgtAO42+V4fEpVV4nIfGAd5mnD9VTDkiYi8gHQFWgkIhnAGGAi8KGI3ItJpDd75Vy2lIllWZblCduFZVmWZXnEJhDLsizLIzaBWJZlWR6xCcSyLMvyiE0glmVZlkdsArGsKkBEVrgdg2UVZx/jtSzLsjxiWyCW5UUicomIpIpIsIic7cw9EVnCdp+IyFpn/X3OshbOfA2NRCRARL4VkeucdUedr+eLSKKIfO/MaXGVf6/Qsv7LtkAsy8tEZDwQDIRg6k09X8I2DVV1v4iEYMrlXK2qWSIyGDOXwyrgYlUd4mx/VFXrisijQLCqTnDmqqnjlN23LL+zCcSyvMwpDbIayAGuUNX8ErYZC/Rz3rYErlfVZGfdEuBiIOZkciiSQOKAmcB7wCeq+n3xY1uWv9guLMvyvoZAXaAepiVyChHpiqkMe7mqRmNqMgU76+pgKj7jHOMUzmRBccAvwLsicqcP4resCrEJxLK8bzrwNDAH+GcJ6/8EHFDVYyLSFjN18En/dPZ7Bnir+I4i0gIzd8lbmKrJ1b0Eu1WJ2Wq8luVFTosgT1Xfd+5RrBCRbqq6rMhmi4H7RSQV+BE42XV1NXAJcKWq5ovI30XkblVNKLJvV+AxEckFjgK2BWK5xt4DsSzLsjxiu7Asy7Isj9gEYlmWZXnEJhDLsizLIzaBWJZlWR6xCcSyLMvyiE0glmVZlkdsArEsy7I88v9od2X1K+5KDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, siny, label=\"sin(x)\")\n",
    "plt.plot(x, cosy, color=\"red\", linestyle=\"--\", label=\"cos(x)\")\n",
    "plt.legend()\n",
    "plt.title(\"welcome to the ML Word!\")\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13e5e408470>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, siny)       # 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13e5e992d30>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(0, 1, 100)\n",
    "y = np.random.normal(0, 1, 100)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
